FIG. 1 illustrates one example of an architecture of a device for system diagnostics according to the prior art. The system, referred to as system under diagnosis, includes various equipment units. The diagnostic device typically includes: means 101 for monitoring the equipment, the means generating observation messages O1, O2, . . . , On based on effects coming from the system 100; means 102 for determining a set of observations Eobs from the observation messages O1, O2, . . . , On coming from the monitoring means 101; means 103 for determining fault acknowledgements 107 from observations O′1, O′2, . . . , O′k from the set of observations Eobs and a set of logical relationships 108 between the observations and the causes having generated the observed effects; and means 104 for determining maintenance operations from the fault acknowledgements 107.
The set of logical relationships 108 is typically produced by the designer.
The monitoring means 101 are, for example, implemented by physical sensors or logical functions of the equipment MONITORING type.
The observations used may denote information on correct operation or on malfunctioning. The presence or the absence of a message is therefore interpreted differently depending on the type of operation observed. Indeed, during normal operation, certain messages are present and others are absent. For example, a unit of equipment may indicate that it is operating correctly by a periodic “healthy” message, and it may also indicate a malfunction, when this is detected, by a “fault_XX” message. The absence of a “healthy” message is therefore interpreted differently from the absence of a “fault_XX” message.
One of the drawbacks of such a device is the difficulty in establishing a set of logical relationships 108 that will provide a reliable and accurate diagnostic. This problem is solved by using a formal description of the system.
From this formal description, various diagnostic methods may be deduced that consume different amounts of memory or processing resources. A method known as RBR (Rule-based reasoning) or a method known as MBR (Model-based reasoning) may for example be mentioned.
A first diagnostic method, known as MBR, may be implemented by using a model explorer, one exemplary embodiment of which is illustrated in FIG. 2b. The model explorer 202 allows the direct exploration of the behavioral model 203 of the system and the extraction of events having led to the observation of the effects by the monitoring means 101 in the form of sequences. The determination of fault acknowledgements 107 is located onboard the aircraft and is carried out during the flight. The search for a cause is carried out by dynamic exploration of the model 203 during the flight.
This method uses a model editor 201 allowing the behavioral model to be generated or edited based on a description 204 of the system under diagnosis and the observations.
This method has the advantage of providing a sequence of events causing the observed effects and the possibility of taking into account, where appropriate, modifications to the system 100, due for example to reconfigurations during the flight, these modifications having been reproduced by the model 203.
On the other hand, this method can consume a large amount of processing resources and may not be well adapted to certain types of aircraft onboard computers.
A second diagnostic method shown, known as RBR, may be implemented by using a model explorer 202 calculating logical relationships associated with the observations using the behavioral model 203 of the system. The means 103 for determining the fault acknowledgements 107 then comprise means for stringing together the logical relationships.
The determination of the logical relationships is carried out using a memory storage unit 108 associating a logical relationship with each observation. This method also uses a model editor 201 allowing the behavioral model to be generated or edited using a description 204 of the system under diagnosis and the observations.
In this method, the logical relationships contained in the storage unit 108 are produced on the ground, during the design phase, by making use of the behavioral model 203 of the system under diagnosis. Use of the behavioral model 203 is made thanks to a model explorer 202. The model explorer produces said logical relationships by means of the exploration of the behavioral model 203. Exploration is understood to mean: search for and extraction of the information contained in the model.
This method has the advantage of being executable by most onboard computers, but on the other hand, it no longer provides any explanation of the order of the observed effects in the form of a sequence of events and no longer allows the changes to the system 100 (e.g. a reconfiguration) to be taken into account.
By way of example, a method called CBR (for Case-based reasoning) may also be mentioned in which a failure is identified by its signature, i.e. a set of observations; then, approximating signatures are sought in a database. Such a method requires: (i) accumulation of experience so as to build a database of the known failures; and (ii) finding a “similarity function” that allows it to be said that one signature is closer to this signature than another. One variant of the MBR method may be mentioned in which the model is a network of statistical dependencies between failure and observations. For example, if an event P is observed, then there is x chance that the failure is A and y chance that it is B. This method requires statistical values to be obtained which assumes an accumulation of experience.
It is recalled that a maintenance function comprises various processing means that may be implemented on one or more computers. These computers equip the system under diagnosis or are dedicated maintenance equipment.
The system under diagnosis is an assembly of processing units collaborating with one another to supply a service. A processing unit may itself be seen as a system and hence as an assembly of processing units. A system may therefore be decomposed in a hierarchical manner by successively considering the assemblies of processing units. Thus, a system under diagnosis may be considered according to various hierarchical levels corresponding to the various assemblies of processing units.
The system under diagnosis comprises a plurality of processing units. It is decomposed into various hierarchical levels into which these various processing units are grouped, a first hierarchical level being said to be lower than a second level when the first level comprises processing units composing processing units of the second level.
These various processing units are non-uniform in terms of processing or memory resources. In addition, certain processing units are not sufficiently powerful to implement the first MBR diagnostic method based on the models. The designer of the system must then deal with the problem of coherence of the diagnostics carried out by all the sub-systems and sent to the highest hierarchical level for consolidation.
It is thus desirable to maintain coherence between the logical relationships contained in the memory units 108 of each lower hierarchical level and the known global model 203 of the highest hierarchical level.